|Title||Monitoring Patients in Hospital Beds Using Unobtrusive Depth Sensors|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Tanvi Banerjee, Moein Enayati, James Keller, Marjorie Skubic, Mihail Popescu, Marilyn Rantz|
|Conference Name||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society|
|Pagination||5904 - 5907|
|Conference Location||Chicago, IL|
We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner. An existing fall detection algorithm is currently generating fall alerts in several rooms in the University of Missouri Hospital (MUH). In this paper we describe a technique to reduce false alerts such as pillows falling off the bed or equipment movement. We do so by detecting the presence of the patient in the bed for the times when the fall alert is generated. We test our algorithm on 96 hours obtained in two hospital rooms from MUH.